In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import confusion_matrix,accuracy_score
from sklearn.preprocessing import MinMaxScaler
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')

DATA PREPROCESSING

Importing Datasets

Data Description: id - Represents different categories of engines range: (1 to 100) cycle - time in cycles setting - operation settings s - sensor measurement RUL - Remaining Useful Life ttf - trajectory factor
In [2]:
import os, types
import pandas as pd
from botocore.client import Config
import ibm_boto3

def __iter__(self): return 0

# @hidden_cell
# The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials.
# You might want to remove those credentials before you share the notebook.

if os.environ.get('RUNTIME_ENV_LOCATION_TYPE') == 'external':
    endpoint_4b15fa3394a14800b00716fbf214f282 = 'https://s3.us.cloud-object-storage.appdomain.cloud'
else:
    endpoint_4b15fa3394a14800b00716fbf214f282 = 'https://s3.private.us.cloud-object-storage.appdomain.cloud'

client_4b15fa3394a14800b00716fbf214f282 = ibm_boto3.client(service_name='s3',
    ibm_api_key_id='UK9E4LhblYhtCOzQ9wMS3cP0ALg0Ne_Z_neaRkm7s5zn',
    ibm_auth_endpoint="https://iam.cloud.ibm.com/oidc/token",
    config=Config(signature_version='oauth'),
    endpoint_url=endpoint_4b15fa3394a14800b00716fbf214f282)

body = client_4b15fa3394a14800b00716fbf214f282.get_object(Bucket='predictenginefailures-donotdelete-pr-dzldltk75qmkfh',Key='df_train.csv')['Body']
# add missing __iter__ method, so pandas accepts body as file-like object
if not hasattr(body, "__iter__"): body.__iter__ = types.MethodType( __iter__, body )
df_train = pd.read_csv(body)


body = client_4b15fa3394a14800b00716fbf214f282.get_object(Bucket='predictenginefailures-donotdelete-pr-dzldltk75qmkfh',Key='df_test.csv')['Body']
# add missing __iter__ method, so pandas accepts body as file-like object
if not hasattr(body, "__iter__"): body.__iter__ = types.MethodType( __iter__, body )
df_test = pd.read_csv(body)


body = client_4b15fa3394a14800b00716fbf214f282.get_object(Bucket='predictenginefailures-donotdelete-pr-dzldltk75qmkfh',Key='df_true.csv')['Body']
# add missing __iter__ method, so pandas accepts body as file-like object
if not hasattr(body, "__iter__"): body.__iter__ = types.MethodType( __iter__, body )

df_truth = pd.read_csv(body)
In [3]:
print('Shape:',df_train.shape)
print(df_train.info())
df_train.head()

Shape: (20631, 26)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20631 entries, 0 to 20630
Data columns (total 26 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   engine_id  20631 non-null  int64  
 1   cycle      20631 non-null  int64  
 2   setting1   20631 non-null  float64
 3   setting2   20631 non-null  float64
 4   setting3   20631 non-null  int64  
 5   s1         20631 non-null  float64
 6   s2         20631 non-null  float64
 7   s3         20631 non-null  float64
 8   s4         20631 non-null  float64
 9   s5         20631 non-null  float64
 10  s6         20631 non-null  float64
 11  s7         20631 non-null  float64
 12  s8         20631 non-null  float64
 13  s9         20631 non-null  float64
 14  s10        20631 non-null  float64
 15  s11        20631 non-null  float64
 16  s12        20631 non-null  float64
 17  s13        20631 non-null  float64
 18  s14        20631 non-null  float64
 19  s15        20631 non-null  float64
 20  s16        20631 non-null  float64
 21  s17        20631 non-null  int64  
 22  s18        20631 non-null  int64  
 23  s19        20631 non-null  int64  
 24  s20        20631 non-null  float64
 25  s21        20631 non-null  float64
dtypes: float64(20), int64(6)
memory usage: 4.1 MB
None
Out[3]:
engine_id cycle setting1 setting2 setting3 s1 s2 s3 s4 s5 ... s12 s13 s14 s15 s16 s17 s18 s19 s20 s21
0 1 1 -0.0007 -0.0004 100 518.67 641.82 1589.70 1400.60 14.62 ... 521.66 2388.02 8138.62 8.4195 0.03 392 2388 100 39.06 23.4190
1 1 2 0.0019 -0.0003 100 518.67 642.15 1591.82 1403.14 14.62 ... 522.28 2388.07 8131.49 8.4318 0.03 392 2388 100 39.00 23.4236
2 1 3 -0.0043 0.0003 100 518.67 642.35 1587.99 1404.20 14.62 ... 522.42 2388.03 8133.23 8.4178 0.03 390 2388 100 38.95 23.3442
3 1 4 0.0007 0.0000 100 518.67 642.35 1582.79 1401.87 14.62 ... 522.86 2388.08 8133.83 8.3682 0.03 392 2388 100 38.88 23.3739
4 1 5 -0.0019 -0.0002 100 518.67 642.37 1582.85 1406.22 14.62 ... 522.19 2388.04 8133.80 8.4294 0.03 393 2388 100 38.90 23.4044

5 rows × 26 columns

In [4]:
print('Shape:',df_test.shape)
print(df_test.info())
df_test.head()

Shape: (13096, 26)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 13096 entries, 0 to 13095
Data columns (total 26 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   engine_id  13096 non-null  int64  
 1   cycle      13096 non-null  int64  
 2   setting1   13096 non-null  float64
 3   setting2   13096 non-null  float64
 4   setting3   13096 non-null  int64  
 5   s1         13096 non-null  float64
 6   s2         13096 non-null  float64
 7   s3         13096 non-null  float64
 8   s4         13096 non-null  float64
 9   s5         13096 non-null  float64
 10  s6         13096 non-null  float64
 11  s7         13096 non-null  float64
 12  s8         13096 non-null  float64
 13  s9         13096 non-null  float64
 14  s10        13096 non-null  float64
 15  s11        13096 non-null  float64
 16  s12        13096 non-null  float64
 17  s13        13096 non-null  float64
 18  s14        13096 non-null  float64
 19  s15        13096 non-null  float64
 20  s16        13096 non-null  float64
 21  s17        13096 non-null  int64  
 22  s18        13096 non-null  int64  
 23  s19        13096 non-null  int64  
 24  s20        13096 non-null  float64
 25  s21        13096 non-null  float64
dtypes: float64(20), int64(6)
memory usage: 2.6 MB
None
Out[4]:
engine_id cycle setting1 setting2 setting3 s1 s2 s3 s4 s5 ... s12 s13 s14 s15 s16 s17 s18 s19 s20 s21
0 1 1 0.0023 0.0003 100 518.67 643.02 1585.29 1398.21 14.62 ... 521.72 2388.03 8125.55 8.4052 0.03 392 2388 100 38.86 23.3735
1 1 2 -0.0027 -0.0003 100 518.67 641.71 1588.45 1395.42 14.62 ... 522.16 2388.06 8139.62 8.3803 0.03 393 2388 100 39.02 23.3916
2 1 3 0.0003 0.0001 100 518.67 642.46 1586.94 1401.34 14.62 ... 521.97 2388.03 8130.10 8.4441 0.03 393 2388 100 39.08 23.4166
3 1 4 0.0042 0.0000 100 518.67 642.44 1584.12 1406.42 14.62 ... 521.38 2388.05 8132.90 8.3917 0.03 391 2388 100 39.00 23.3737
4 1 5 0.0014 0.0000 100 518.67 642.51 1587.19 1401.92 14.62 ... 522.15 2388.03 8129.54 8.4031 0.03 390 2388 100 38.99 23.4130

5 rows × 26 columns

In [5]:
print('Shape:',df_truth.shape)
print(df_truth.info())
df_truth.head()

Shape: (100, 2)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 100 entries, 0 to 99
Data columns (total 2 columns):
 #   Column      Non-Null Count  Dtype
---  ------      --------------  -----
 0   rem_cycles  100 non-null    int64
 1   engine_id   100 non-null    int64
dtypes: int64(2)
memory usage: 1.7 KB
None
Out[5]:
rem_cycles engine_id
0 112 1
1 98 2
2 69 3
3 82 4
4 91 5
There is no null value in any of the dataset There are no categorical variables in the dataset

PRE PROCESS THE DATASET

In [6]:
#Boxplots of sensor data
plt.figure(figsize = (16, 21))
for i in range(21):
    temp_data = df_train.iloc[:,i+5]
    plt.subplot(7,3,i+1)
    plt.boxplot(temp_data)
    plt.title("Sensor " + str(i+1) + ", column "+ str(i+6))
plt.show()
In [7]:
# comparision of train and test data
plt.figure(figsize = (15, 21))
for i,j in enumerate([6, 7, 8, 11, 12, 13, 15, 16, 17, 18, 19, 21, 24, 25]):
    temp_train = df_train.iloc[:, j]
    temp_test = df_test.iloc[:, j]
    plt.subplot(7,3, i+1)
    sns.kdeplot(temp_train, legend = False, color = "blue", label = "Train")
    sns.kdeplot(temp_test, legend = False, color = "red", label = "Test")
    plt.title("Sensor " + str(j-4) + ", column "+ str(j+1))
    plt.legend()
plt.show()
In [8]:
#plt.style.use('dark_background')
plt.figure(figsize=(20,50))
ax = df_train.groupby('engine_id')['cycle'].max().plot(kind='barh',width=0.8,stacked = True, align = 'center', rot = 0)
plt.title("Engine Lifetime", fontweight = 'bold', size = 35)
plt.xlabel('Cycle Time', fontweight='bold',size=30)
plt.xticks(size=25)
plt.ylabel('Engine ID',fontweight='bold',size=30)
plt.yticks(size=25)
plt.grid(True)
plt.tight_layout(True)
plt.show()
In [7]:
df_train.engine_id.unique()
Out[7]:
array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,
        27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,
        40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,
        53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65,
        66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,
        79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,
        92,  93,  94,  95,  96,  97,  98,  99, 100])
1. The last cycle in training data in each time series can be considered as the failure point of the corresponding engine. 2. The test data does not indicate when the failure occurs (in other words, the last time period does NOT represent the failure point). 3. Instead of having all time series data in the testing data, we only keep the the record with the maximum cycles performed by each engine id. 4. Eventually, the testing data will contains 100 records, which matches the RUL in the ground truth data.
In [8]:
rul=pd.DataFrame(df_test.groupby('engine_id')['cycle'].max()).reset_index()
cols=['engine_id','max_cycles'] # maximum number of cycles performed by each engine
rul.columns=cols
print('Shape:',rul.shape)
rul

Shape: (100, 2)
Out[8]:
engine_id max_cycles
0 1 31
1 2 49
2 3 126
3 4 106
4 5 98
... ... ...
95 96 97
96 97 134
97 98 121
98 99 97
99 100 198

100 rows × 2 columns

Now rul and truth data have same dimensions

In [9]:
# calculating remaining time for failure for each engine
df_truth['rtf']=df_truth['rem_cycles']+rul['max_cycles']
print('Shape:',df_truth.shape)
df_truth.head()

Shape: (100, 3)
Out[9]:
rem_cycles engine_id rtf
0 112 1 143
1 98 2 147
2 69 3 195
3 82 4 188
4 91 5 189
In [10]:
df_truth.drop('rem_cycles', axis=1, inplace=True)
df_test=df_test.merge(df_truth,on=['engine_id'],how='left')
df_test['ttf']=df_test['rtf'] - df_test['cycle']
df_test.drop('rtf', axis=1, inplace=True)
print('Shape:',df_test.shape)
df_test.head()

Shape: (13096, 27)
Out[10]:
engine_id cycle setting1 setting2 setting3 s1 s2 s3 s4 s5 ... s13 s14 s15 s16 s17 s18 s19 s20 s21 ttf
0 1 1 0.0023 0.0003 100 518.67 643.02 1585.29 1398.21 14.62 ... 2388.03 8125.55 8.4052 0.03 392 2388 100 38.86 23.3735 142
1 1 2 -0.0027 -0.0003 100 518.67 641.71 1588.45 1395.42 14.62 ... 2388.06 8139.62 8.3803 0.03 393 2388 100 39.02 23.3916 141
2 1 3 0.0003 0.0001 100 518.67 642.46 1586.94 1401.34 14.62 ... 2388.03 8130.10 8.4441 0.03 393 2388 100 39.08 23.4166 140
3 1 4 0.0042 0.0000 100 518.67 642.44 1584.12 1406.42 14.62 ... 2388.05 8132.90 8.3917 0.03 391 2388 100 39.00 23.3737 139
4 1 5 0.0014 0.0000 100 518.67 642.51 1587.19 1401.92 14.62 ... 2388.03 8129.54 8.4031 0.03 390 2388 100 38.99 23.4130 138

5 rows × 27 columns

In [11]:
df_train['ttf'] = df_train.groupby(['engine_id'])['cycle'].transform(max)-df_train['cycle']
print('Shape:',df_train.shape)
df_train.head()

Shape: (20631, 27)
Out[11]:
engine_id cycle setting1 setting2 setting3 s1 s2 s3 s4 s5 ... s13 s14 s15 s16 s17 s18 s19 s20 s21 ttf
0 1 1 -0.0007 -0.0004 100 518.67 641.82 1589.70 1400.60 14.62 ... 2388.02 8138.62 8.4195 0.03 392 2388 100 39.06 23.4190 191
1 1 2 0.0019 -0.0003 100 518.67 642.15 1591.82 1403.14 14.62 ... 2388.07 8131.49 8.4318 0.03 392 2388 100 39.00 23.4236 190
2 1 3 -0.0043 0.0003 100 518.67 642.35 1587.99 1404.20 14.62 ... 2388.03 8133.23 8.4178 0.03 390 2388 100 38.95 23.3442 189
3 1 4 0.0007 0.0000 100 518.67 642.35 1582.79 1401.87 14.62 ... 2388.08 8133.83 8.3682 0.03 392 2388 100 38.88 23.3739 188
4 1 5 -0.0019 -0.0002 100 518.67 642.37 1582.85 1406.22 14.62 ... 2388.04 8133.80 8.4294 0.03 393 2388 100 38.90 23.4044 187

5 rows × 27 columns

In [12]:
df_train.ttf.value_counts()
Out[12]:
0      100
123    100
121    100
89     100
73     100
      ... 
341      1
356      1
355      1
354      1
351      1
Name: ttf, Length: 362, dtype: int64

Calculating Time to Failure

Set a threshold of 30. 1 - engine fails 0 - does not fail This is done so that our model can predict whether an engine will fail in the next days.
In [13]:
dfn_train=df_train.copy() # new train data
dfn_test=df_test.copy() # new test data
period=30 # threshold
dfn_train['label_bc'] = dfn_train['ttf'].apply(lambda x: 1 if x <= period else 0)
dfn_test['label_bc'] = dfn_test['ttf'].apply(lambda x: 1 if x <= period else 0)
print('For train data-')
print('min ttf:',dfn_train.ttf.min())
print('max ttf:',dfn_train.ttf.max())
print('mean ttf:',dfn_train.ttf.mean())
dfn_train.head()

For train data-
min ttf: 0
max ttf: 361
mean ttf: 107.80786195530997
Out[13]:
engine_id cycle setting1 setting2 setting3 s1 s2 s3 s4 s5 ... s14 s15 s16 s17 s18 s19 s20 s21 ttf label_bc
0 1 1 -0.0007 -0.0004 100 518.67 641.82 1589.70 1400.60 14.62 ... 8138.62 8.4195 0.03 392 2388 100 39.06 23.4190 191 0
1 1 2 0.0019 -0.0003 100 518.67 642.15 1591.82 1403.14 14.62 ... 8131.49 8.4318 0.03 392 2388 100 39.00 23.4236 190 0
2 1 3 -0.0043 0.0003 100 518.67 642.35 1587.99 1404.20 14.62 ... 8133.23 8.4178 0.03 390 2388 100 38.95 23.3442 189 0
3 1 4 0.0007 0.0000 100 518.67 642.35 1582.79 1401.87 14.62 ... 8133.83 8.3682 0.03 392 2388 100 38.88 23.3739 188 0
4 1 5 -0.0019 -0.0002 100 518.67 642.37 1582.85 1406.22 14.62 ... 8133.80 8.4294 0.03 393 2388 100 38.90 23.4044 187 0

5 rows × 28 columns

In [14]:
dfn_train.label_bc.value_counts()
Out[14]:
0    17531
1     3100
Name: label_bc, dtype: int64

Feature Scaling

In [15]:
features_col_name=['setting1', 'setting2', 'setting3', 's1', 's2', 's3', 's4', 's5', 's6', 's7', 's8', 's9', 's10', 's11',
                   's12', 's13', 's14', 's15', 's16', 's17', 's18', 's19', 's20', 's21']
target_col_name='label_bc'
sc=MinMaxScaler()
dfn_train[features_col_name]=sc.fit_transform(dfn_train[features_col_name])
dfn_test[features_col_name]=sc.transform(dfn_test[features_col_name])
dfn_train.head()
Out[15]:
engine_id cycle setting1 setting2 setting3 s1 s2 s3 s4 s5 ... s14 s15 s16 s17 s18 s19 s20 s21 ttf label_bc
0 1 1 0.459770 0.166667 0.0 0.0 0.183735 0.406802 0.309757 0.0 ... 0.199608 0.363986 0.0 0.333333 0.0 0.0 0.713178 0.724662 191 0
1 1 2 0.609195 0.250000 0.0 0.0 0.283133 0.453019 0.352633 0.0 ... 0.162813 0.411312 0.0 0.333333 0.0 0.0 0.666667 0.731014 190 0
2 1 3 0.252874 0.750000 0.0 0.0 0.343373 0.369523 0.370527 0.0 ... 0.171793 0.357445 0.0 0.166667 0.0 0.0 0.627907 0.621375 189 0
3 1 4 0.540230 0.500000 0.0 0.0 0.343373 0.256159 0.331195 0.0 ... 0.174889 0.166603 0.0 0.333333 0.0 0.0 0.573643 0.662386 188 0
4 1 5 0.390805 0.333333 0.0 0.0 0.349398 0.257467 0.404625 0.0 ... 0.174734 0.402078 0.0 0.416667 0.0 0.0 0.589147 0.704502 187 0

5 rows × 28 columns

In [31]:
sns.pairplot(dfn_train)
plt.plot()
# Plotting pairwise relationships in a dataset.
Out[31]:
[]

Splitting dependent and independent variables

x_train represents the independent variables All the clolumns except label_bc are independent variables y_train represents the dependent variable (label_bc)
In [16]:
x_train = dfn_train.iloc[:,:-1].values
y_train = dfn_train.iloc[:,-1:].values
In [17]:
print(type(x_train))
print(type(y_train))

<class 'numpy.ndarray'>
<class 'numpy.ndarray'>
In [18]:
x_test = dfn_test.iloc[:,:-1].values
y_test = dfn_test.iloc[:,-1:].values

Model Building

In [19]:
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB,MultinomialNB
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV

Hyperparameter Tuning

In [20]:
model_params = {
    'svm': {
        'model': SVC(),
        'params' : {
            'C': [1,10,20],
            'kernel': ['rbf','linear']
        }  
    },
    'random_forest': {
        'model': RandomForestClassifier(),
        'params' : {
            'criterion': ['gini','entropy']
        }
    },
    'logistic_regression' : {
        'model': LogisticRegression(),
        'params': {
            'C': [1,5,10],
            'solver': ['sag', 'saga','lbfgs','liblinear']
        }
    },
    'naive_bayes_gaussian': {
        'model': GaussianNB(),
        'params': {}
    },
    'decision_tree': {
        'model': DecisionTreeClassifier(),
        'params': {
            'criterion': ['gini','entropy'],
            
        }
    },
    'KNN': {
        'model': KNeighborsClassifier(),
        'params': {
            'n_neighbors': [2,5,7]
        }
    }
}
In [21]:
scores = []

for model_name, mp in model_params.items():
    clf =  GridSearchCV(mp['model'], mp['params'], cv=3)
    clf.fit(x_train,y_train)
    scores.append({
        'model': model_name,
        'best_score': clf.best_score_,
        'best_params': clf.best_params_
    })
    
df_score = pd.DataFrame(scores,columns=['model','best_score','best_params'])
df_score
Out[21]:
model best_score best_params
0 svm 1.000000 {'C': 1, 'kernel': 'linear'}
1 random_forest 1.000000 {'criterion': 'gini'}
2 logistic_regression 0.998837 {'C': 1, 'solver': 'lbfgs'}
3 naive_bayes_gaussian 0.937909 {}
4 decision_tree 1.000000 {'criterion': 'gini'}
5 KNN 0.975910 {'n_neighbors': 7}

Fitting Model

In [22]:
lr_model=LogisticRegression(C=1,solver='lbfgs')
lr_model.fit(x_train,y_train)
Out[22]:
LogisticRegression(C=1)
In [23]:
y_pred=lr_model.predict(x_test)

Model Evaluation

In [24]:
from sklearn.metrics import accuracy_score,confusion_matrix
accuracy_score(y_pred,y_test)
Out[24]:
0.9993891264508247

The accuracy of the model on the test data is 99%

In [25]:
cm = confusion_matrix(y_test,y_pred)
cm
Out[25]:
array([[12763,     1],
       [    7,   325]])
In [26]:
plt.figure(figsize=(9,7))
sns.heatmap(cm,annot=True,linewidths=2,linecolor='orange')
plt.xlabel('Predicted')
plt.ylabel('Truth')
plt.title('Confusion Matrix')
plt.show()

Deploy Model

In [26]:
!pip install ibm_watson_machine_learning

/opt/conda/envs/Python-3.7-OpenCE/lib/python3.7/site-packages/secretstorage/dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead
  from cryptography.utils import int_from_bytes
/opt/conda/envs/Python-3.7-OpenCE/lib/python3.7/site-packages/secretstorage/util.py:25: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead
  from cryptography.utils import int_from_bytes
Requirement already satisfied: ibm_watson_machine_learning in /opt/conda/envs/Python-3.7-OpenCE/lib/python3.7/site-packages (1.0.105)
Requirement already satisfied: lomond in /opt/conda/envs/Python-3.7-OpenCE/lib/python3.7/site-packages (from ibm_watson_machine_learning) (0.3.3)
Requirement already satisfied: requests in /opt/conda/envs/Python-3.7-OpenCE/lib/python3.7/site-packages (from ibm_watson_machine_learning) (2.22.0)
Requirement already satisfied: urllib3 in /opt/conda/envs/Python-3.7-OpenCE/lib/python3.7/site-packages (from ibm_watson_machine_learning) (1.25.9)
Requirement already satisfied: pandas<1.3.0,>=0.24.2 in /opt/conda/envs/Python-3.7-OpenCE/lib/python3.7/site-packages (from ibm_watson_machine_learning) (1.0.5)
Requirement already satisfied: ibm-cos-sdk==2.7.* in /opt/conda/envs/Python-3.7-OpenCE/lib/python3.7/site-packages (from ibm_watson_machine_learning) (2.7.0)
Requirement already satisfied: certifi in /opt/conda/envs/Python-3.7-OpenCE/lib/python3.7/site-packages (from ibm_watson_machine_learning) (2021.5.30)
Requirement already satisfied: tabulate in /opt/conda/envs/Python-3.7-OpenCE/lib/python3.7/site-packages (from ibm_watson_machine_learning) (0.8.3)
Requirement already satisfied: packaging in /opt/conda/envs/Python-3.7-OpenCE/lib/python3.7/site-packages (from ibm_watson_machine_learning) (20.4)
Requirement already satisfied: six>=1.10.0 in /opt/conda/envs/Python-3.7-OpenCE/lib/python3.7/site-packages (from lomond->ibm_watson_machine_learning) (1.15.0)
Requirement already satisfied: idna<2.9,>=2.5 in /opt/conda/envs/Python-3.7-OpenCE/lib/python3.7/site-packages (from requests->ibm_watson_machine_learning) (2.8)
Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/envs/Python-3.7-OpenCE/lib/python3.7/site-packages (from requests->ibm_watson_machine_learning) (3.0.4)
Requirement already satisfied: numpy>=1.13.3 in /opt/conda/envs/Python-3.7-OpenCE/lib/python3.7/site-packages (from pandas<1.3.0,>=0.24.2->ibm_watson_machine_learning) (1.19.2)
Requirement already satisfied: python-dateutil>=2.6.1 in /opt/conda/envs/Python-3.7-OpenCE/lib/python3.7/site-packages (from pandas<1.3.0,>=0.24.2->ibm_watson_machine_learning) (2.8.1)
Requirement already satisfied: pytz>=2017.2 in /opt/conda/envs/Python-3.7-OpenCE/lib/python3.7/site-packages (from pandas<1.3.0,>=0.24.2->ibm_watson_machine_learning) (2020.1)
Requirement already satisfied: ibm-cos-sdk-core==2.7.0 in /opt/conda/envs/Python-3.7-OpenCE/lib/python3.7/site-packages (from ibm-cos-sdk==2.7.*->ibm_watson_machine_learning) (2.7.0)
Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /opt/conda/envs/Python-3.7-OpenCE/lib/python3.7/site-packages (from ibm-cos-sdk==2.7.*->ibm_watson_machine_learning) (0.9.4)
Requirement already satisfied: ibm-cos-sdk-s3transfer==2.7.0 in /opt/conda/envs/Python-3.7-OpenCE/lib/python3.7/site-packages (from ibm-cos-sdk==2.7.*->ibm_watson_machine_learning) (2.7.0)
Requirement already satisfied: pyparsing>=2.0.2 in /opt/conda/envs/Python-3.7-OpenCE/lib/python3.7/site-packages (from packaging->ibm_watson_machine_learning) (2.4.7)
Requirement already satisfied: docutils<0.16,>=0.10 in /opt/conda/envs/Python-3.7-OpenCE/lib/python3.7/site-packages (from ibm-cos-sdk-core==2.7.0->ibm-cos-sdk==2.7.*->ibm_watson_machine_learning) (0.15.2)
In [27]:
from ibm_watson_machine_learning import APIClient
wml_credentials = {
    'url': "https://us-south.ml.cloud.ibm.com",
    'apikey': 'zT1z5T6S4CG9SN6369lVbajORLf64sDZnN2TSMFLdxRt'
}
client=APIClient(wml_credentials)
In [28]:
def guid_from_space_name(client,space_name):
    space=client.spaces.get_details()
    return(next(item for item in space['resources'] if item['entity']["name"] == space_name)['metadata']['id'])
In [29]:
space_uid=guid_from_space_name(client,'Models')
print('Space UID = '+space_uid)

Space UID = 870d9e91-fc05-4c2c-bae5-959e6cc551af
In [30]:
client.set.default_space(space_uid)
Out[30]:
'SUCCESS'
In [31]:
client.software_specifications.list()

-----------------------------  ------------------------------------  ----
NAME                           ASSET_ID                              TYPE
default_py3.6                  0062b8c9-8b7d-44a0-a9b9-46c416adcbd9  base
pytorch-onnx_1.3-py3.7-edt     069ea134-3346-5748-b513-49120e15d288  base
scikit-learn_0.20-py3.6        09c5a1d0-9c1e-4473-a344-eb7b665ff687  base
spark-mllib_3.0-scala_2.12     09f4cff0-90a7-5899-b9ed-1ef348aebdee  base
ai-function_0.1-py3.6          0cdb0f1e-5376-4f4d-92dd-da3b69aa9bda  base
shiny-r3.6                     0e6e79df-875e-4f24-8ae9-62dcc2148306  base
tensorflow_2.4-py3.7-horovod   1092590a-307d-563d-9b62-4eb7d64b3f22  base
pytorch_1.1-py3.6              10ac12d6-6b30-4ccd-8392-3e922c096a92  base
tensorflow_1.15-py3.6-ddl      111e41b3-de2d-5422-a4d6-bf776828c4b7  base
scikit-learn_0.22-py3.6        154010fa-5b3b-4ac1-82af-4d5ee5abbc85  base
default_r3.6                   1b70aec3-ab34-4b87-8aa0-a4a3c8296a36  base
pytorch-onnx_1.3-py3.6         1bc6029a-cc97-56da-b8e0-39c3880dbbe7  base
tensorflow_2.1-py3.6           1eb25b84-d6ed-5dde-b6a5-3fbdf1665666  base
tensorflow_2.4-py3.8-horovod   217c16f6-178f-56bf-824a-b19f20564c49  base
autoai-ts_3.8-py3.8            2aa0c932-798f-5ae9-abd6-15e0c2402fb5  base
tensorflow_1.15-py3.6          2b73a275-7cbf-420b-a912-eae7f436e0bc  base
pytorch_1.2-py3.6              2c8ef57d-2687-4b7d-acce-01f94976dac1  base
spark-mllib_2.3                2e51f700-bca0-4b0d-88dc-5c6791338875  base
pytorch-onnx_1.1-py3.6-edt     32983cea-3f32-4400-8965-dde874a8d67e  base
spark-mllib_3.0-py37           36507ebe-8770-55ba-ab2a-eafe787600e9  base
spark-mllib_2.4                390d21f8-e58b-4fac-9c55-d7ceda621326  base
xgboost_0.82-py3.6             39e31acd-5f30-41dc-ae44-60233c80306e  base
pytorch-onnx_1.2-py3.6-edt     40589d0e-7019-4e28-8daa-fb03b6f4fe12  base
autoai-obm_3.0                 42b92e18-d9ab-567f-988a-4240ba1ed5f7  base
spark-mllib_2.4-r_3.6          49403dff-92e9-4c87-a3d7-a42d0021c095  base
xgboost_0.90-py3.6             4ff8d6c2-1343-4c18-85e1-689c965304d3  base
pytorch-onnx_1.1-py3.6         50f95b2a-bc16-43bb-bc94-b0bed208c60b  base
spark-mllib_2.4-scala_2.11     55a70f99-7320-4be5-9fb9-9edb5a443af5  base
autoai-obm_2.0                 5c2e37fa-80b8-5e77-840f-d912469614ee  base
spss-modeler_18.1              5c3cad7e-507f-4b2a-a9a3-ab53a21dee8b  base
autoai-kb_3.1-py3.7            632d4b22-10aa-5180-88f0-f52dfb6444d7  base
pytorch-onnx_1.7-py3.8         634d3cdc-b562-5bf9-a2d4-ea90a478456b  base
spark-mllib_2.3-r_3.6          6586b9e3-ccd6-4f92-900f-0f8cb2bd6f0c  base
tensorflow_2.4-py3.7           65e171d7-72d1-55d9-8ebb-f813d620c9bb  base
spss-modeler_18.2              687eddc9-028a-4117-b9dd-e57b36f1efa5  base
pytorch-onnx_1.2-py3.6         692a6a4d-2c4d-45ff-a1ed-b167ee55469a  base
do_12.9                        75a3a4b0-6aa0-41b3-a618-48b1f56332a6  base
spark-mllib_2.3-scala_2.11     7963efe5-bbec-417e-92cf-0574e21b4e8d  base
spark-mllib_2.4-py37           7abc992b-b685-532b-a122-a396a3cdbaab  base
caffe_1.0-py3.6                7bb3dbe2-da6e-4145-918d-b6d84aa93b6b  base
pytorch-onnx_1.7-py3.7         812c6631-42b7-5613-982b-02098e6c909c  base
cuda-py3.6                     82c79ece-4d12-40e6-8787-a7b9e0f62770  base
tensorflow_1.15-py3.6-horovod  8964680e-d5e4-5bb8-919b-8342c6c0dfd8  base
hybrid_0.1                     8c1a58c6-62b5-4dc4-987a-df751c2756b6  base
pytorch-onnx_1.3-py3.7         8d5d8a87-a912-54cf-81ec-3914adaa988d  base
caffe-ibm_1.0-py3.6            8d863266-7927-4d1e-97d7-56a7f4c0a19b  base
spss-modeler_17.1              902d0051-84bd-4af6-ab6b-8f6aa6fdeabb  base
do_12.10                       9100fd72-8159-4eb9-8a0b-a87e12eefa36  base
do_py3.7                       9447fa8b-2051-4d24-9eef-5acb0e3c59f8  base
spark-mllib_3.0-r_3.6          94bb6052-c837-589d-83f1-f4142f219e32  base
-----------------------------  ------------------------------------  ----
Note: Only first 50 records were displayed. To display more use 'limit' parameter.
In [32]:
software_spec_uid=client.software_specifications.get_uid_by_name('default_py3.7')
software_spec_uid
Out[32]:
'e4429883-c883-42b6-87a8-f419d64088cd'
In [33]:
model_details=client.repository.store_model(model=lr_model,meta_props={
    client.repository.ModelMetaNames.NAME:'Predictive_Maintenance',
    client.repository.ModelMetaNames.TYPE:'scikit-learn_0.23',
    client.repository.ModelMetaNames.SOFTWARE_SPEC_UID: software_spec_uid
})

model_id=client.repository.get_model_uid(model_details)
In [34]:
model_id
Out[34]:
'52f89543-316b-4622-850e-e51d217a3bcc'
In [ ]: